Level Set Methods for Computation in Hybrid Systems
HSCC '00 Proceedings of the Third International Workshop on Hybrid Systems: Computation and Control
Sampling-based motion planning with differential constraints
Sampling-based motion planning with differential constraints
Planning Algorithms
Using Disparity to Enhance Test Generation for Hybrid Systems
TestCom '08 / FATES '08 Proceedings of the 20th IFIP TC 6/WG 6.1 international conference on Testing of Software and Communicating Systems: 8th International Workshop
Hybrid systems: from verification to falsification by combining motion planning and discrete search
Formal Methods in System Design
Coverage-guided test generation for continuous and hybrid systems
Formal Methods in System Design
Reachability-guided sampling for planning under differential constraints
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Test coverage for continuous and hybrid systems
CAV'07 Proceedings of the 19th international conference on Computer aided verification
Formal approaches to analog circuit verification
Proceedings of the Conference on Design, Automation and Test in Europe
Scalable and efficient analog parametric fault identification
Proceedings of the International Conference on Computer-Aided Design
Proceedings of the International Conference on Computer-Aided Design
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We present a methodology to generate goal-oriented test cases for verifying nonlinear analog circuits. We use a learning-based approach to identify the goal regions in circuit's state space. We use the information that we learn to guide the growth of Rapidly-exploring Random Trees (RRTs) towards these goal regions. Compared to previous approaches for test generation, our methodology generates several test cases of the circuit that are more concentrated in the relevant operating regions. We demonstrate the effectiveness of our approach on typical case studies. We show that our methodology can be used to generate test cases for undesirable behavior that was previously hard to detect.